CIO Asia, Singapore: Cognizant’s Automation Leaders Explain the ‘Do, Think and Learn’ Framework for Automation

Sumithra Gomatam and Matthew Smith, Cognizant

“Intelligent automation is not about entirely replacing the human element, but about elevating the role people play in operations and putting businesses on the fast track to success,” write Cognizant’s Sumithra Gomatam, President, Global Process and Platform Solutions, and Matthew Smith, AVP – Automation, Emerging Business Accelerators. “It is also not about replacing underlying IT systems. On the contrary, it offers a non-invasive and cost-effective approach to making rote and repetitive processes digital, instrumented, analyzed and intelligent. That makes intelligent automation all the more relevant in the face of legacy systems that entail extremely high remediation or change costs.” Excerpts:

“Embracing RPA (robotic process automation) may not be a one-size-fits-all solution for businesses. Instead, business leaders should take the time to evaluate their business strategy and build plans to integrate RPA in ways that will help understand current and future opportunities to move forward.

So how should businesses navigate the tricky ‘first steps’ in adopting intelligent automation? RPA can be broken down into three simple areas for businesses to understand the real opportunities and chart the best path forward: I Do, I Think, I Learn.

Systems that “do” so you don’t have to: Once built and tested, libraries of automated tasks can easily be reused or quickly customized to make future automations go faster. Essentially, any rules-based activity that can be applied to different processes and situations is likely to be a viable RPA candidate. Getting RPA right for most companies means understanding the automation vendor landscape, reviewing and prioritizing processes, launching pilots and proofs of concept and, finally, determining the ideal model that will best support them in the long term.

Systems that “think” so you can make decisions autonomously: This next level of automation (systems that think) is able to execute processes much more dynamically than the first horizon of automation technologies, removing most complexities in dynamic decision-making. The magic ingredient here lies in the introduction of logic, which allows these programs to make decisions independently when they encounter exceptions or other variances in the processes they execute. These thinking systems deal far more effectively with less defined processes and unstructured data. In this way, they differ from RPA and other systems that “do,” which operate best with defined, rules-based processes. Natural language processing (NLP) is another example of an automation technology that “thinks”.

Systems that “learn” to make optimal adjustments when variables change: There is a range of fast-evolving technologies that are characterized by their ability to analyze vast amounts of dynamic and unstructured input, as well as execute advanced processes. These learning systems are also quick learners, in that they can apply one set of rules in one situation and then make optimal adjustments when variables change. Now, imagine if machines could ‘learn’ from their human-counterparts, the ability for businesses to improve accuracy and make significantly fewer errors could easily offset the cost of adopting such technologies.

Every business has a vast opportunity to apply all the “do, think and learn” technologies to improve business processes, accelerate outcomes, increase data quality, and enable powerful and predictive analytics. With intelligent automation, neither man nor machine can claim the sole prize in this battle.”

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